Study on the Application of Clustering Method in the Determination of Uncertainty Parameters of SWMM Model

Abstract Storm Water Management Model (SWMM) is one of the most commonly used models in urban flood simulation. However, because the calibration and verification of the model's uncertainty parameters are extremely dependent on the measured flood data, it is difficult to apply the model in areas lacking data. This study proposes a parameter sample clustering method based on peer research results to determine the uncertainty parameters of SWMM, and compares the simulation results with the simulation results of the manual adjustment method based on measured data. The research shows that the Absolute error (AE), Relative error (RE), Nash efficiency coefficient (NSE), and Coefficient of determination (R2) of the water depth simulated by the parameter sample clustering method are 0.040m, 9.08%, 0.949, 0.967 compared with the measured value, respectively. The value of AE, RE, NSE, and R2 of the manual tuning method during the calibration simulation period are 0.066m, 15.95%, 0.881 and 0.924, respectively. Therefore, the parameter sample clustering method has a better simulation effect than manual tuning method, and it can be further promoted in areas without flood data.

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Application of PCSWMM for the 1-D and 1-D–2-D Modeling of Urban Flooding in Damansara Catchment, Malaysia

Applied Sciences ◽

10.3390/app11199300 ◽

2021 ◽

Vol 11 (19) ◽

pp. 9300

Author(s):

Lariyah Mohd Sidek ◽

Lloyd Hock Chye Chua ◽

Aqilah Syasya Mohd Azizi ◽

Hidayah Basri ◽

Aminah Shakirah Jaafar ◽

...

Keyword(s):

River Flow ◽

Flood Hazard ◽

Developed Countries ◽

Coefficient Of Determination ◽

Efficiency Coefficient ◽

Urban Flood ◽

Storm Water Management Model ◽

Flood Depth ◽

Comparison Results ◽

Model Efficiency

Coupled with climate change, the urbanization-driven increase in the frequency and intensity of floods can be seen in both developing and developed countries, and Malaysia is no exemption. As part of flood hazard mitigation, this study aimed to simulate the urban flood scenarios in Malaysia’s urbanized catchments. The flood simulation was performed using the Personal Computer Storm Water Management Model (PCSWMM) modeling of the Damansara catchment as a case study. An integrated hydrologic-hydraulic model was developed for the 1-D river flow modeling and 1-D–2-D drainage overflow modeling. The reliability of the 1-D river flow model was confirmed through the calibration and validation, in which the water level in TTDI Jaya was satisfactorily predicted, supported by the coefficient of determination (R2), Nash–Sutcliffe model efficiency coefficient (NSE), and relative error (RE). The performance of the 1-D–2-D model was further demonstrated based on the flood depth, extent, and risk caused by the drainage overflow. Two scenarios were tested, and the comparison results showed that the current drainage effectively reduced the drainage overflow due to the increased size of drains compared to the historic drainage in 2015. The procedure and findings of this study could serve as references for the application in flood mitigation planning worldwide, especially for developing countries.

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Development and Assessment of Methods for Predicting Rainfall in Unmeasured Watersheds Using the Relation Between Observed and Sensor-Measured Rainfall

Korean Society of Hazard Mitigation ◽

10.9798/kosham.2021.21.6.293 ◽

2021 ◽

Vol 21 (6) ◽

pp. 293-302

Author(s):

Chungdae Lee ◽

Hayong Kim

Keyword(s):

High Reliability ◽

Absolute Error ◽

Coefficient Of Determination ◽

Efficiency Coefficient ◽

Artificial Rainfall ◽

Information And Communication ◽

The Relationship ◽

Precipitation Station ◽

The Internet Of Things

Recently, with the development of information and communication technology and the Internet of Things (IoT), observation technology using sensors is being applied in a variety of ways, such as using a sensor to observe rainfall in an unmeasured area. In this study, the relationship between the rainfall sensor signal (S) and the amount of rainfall (R) was developed through an experiment in an artificial rainfall generator, and the applicability was evaluated through outdoor observation. The coefficient of determination of the relational expression developed through the indoor experiment was 0.95, the mean absolute error was 2.66 mm/hr, the root mean square error was 3.87 mm/hr, the efficiency coefficient was 0.89, and the concordance index was 0.97, showing very high reliability. In the outdoor test results, the error rate was 7.96% when comparing the data from the rainfall sensors in vehicles and the precipitation station, which were not observed at the same location. Despite such errors, it is judged that accurate rainfall observation using a rainfall sensor is possible in an area where a precipitation station is not installed.

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Modeling daily reference evapotranspiration in middle south Saurashtra region of India for monsoon season using most dominant meteorological variables and the FAO-56 Penman-Monteith method

MAUSAM ◽

10.54302/mausam.v68i1.401 ◽

2021 ◽

Vol 68 (1) ◽

pp. 1-8

Author(s):

MANOJ GUNDALIA ◽

MRUGEN DHOLAKIA

Keyword(s):

Reference Evapotranspiration ◽

Monsoon Season ◽

Absolute Error ◽

Alternative Methods ◽

Meteorological Variables ◽

Coefficient Of Determination ◽

Efficiency Coefficient ◽

Study Region ◽

Proposed Model ◽

Meteorological Observations

Many methods are available to estimate reference evapotranspiration (ETo) from standard meteorological observations. The FAO-56 Penman-Monteith method is considered to be the most physical and reliable method and is often used as a standard to verify other empirical methods. However, it needs a lot of different input parameters. Hence, in the present study, a model based on most dominant meteorological variables influencing ETo is proposed to estimate ETo in the Middle South Saurashtra region of Gujarat (India). The performance of five different alternative methods and proposed model is compared with the standard FAO-56 Penman-Monteith method. The five quantitative standard statistical performance evaluation measures, Nash-Sutcliffe efficiency coefficient (E), coefficient of determination (R2), refined Willmott’s index (dr), root mean square of errors-observations standard deviation ratio (RSR) and mean absolute error (MAE) are employed in evaluating the performance of the selected methods and proposed model. The results show that the developed model and Hargreaves and Samani (1985) method with recalibrated parameters provide the most reliable results in estimation of (ETo) and it can be recommended for estimating (ETo) in the study region.

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Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽

10.3390/forecast3010004 ◽

2021 ◽

Vol 3 (1) ◽

pp. 39-55

Author(s):

Rodgers Makwinja ◽

Seyoum Mengistou ◽

Emmanuel Kaunda ◽

Tena Alemiew ◽

Titus Bandulo Phiri ◽

...

Keyword(s):

Time Series ◽

Information Criterion ◽

Coefficient Of Determination ◽

Series Data ◽

Efficiency Coefficient ◽

Arima Models ◽

Catch Per Unit Effort ◽

Annual Fish ◽

Unit Effort ◽

The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

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An interpretable prediction method for university student academic crisis warning

Complex & Intelligent Systems ◽

10.1007/s40747-021-00383-0 ◽

2021 ◽

Author(s):

Zhai Mingyu ◽

Wang Sutong ◽

Wang Yanzhang ◽

Wang Dujuan

Keyword(s):

Academic Achievement ◽

Visual Analysis ◽

Prediction Method ◽

Absolute Error ◽

University Student ◽

Machine Learning Algorithms ◽

Coefficient Of Determination ◽

Achievement Prediction ◽

Academic Achievement Prediction ◽

Education Administrators

AbstractData-driven techniques improve the quality of talent training comprehensively for university by discovering potential academic problems and proposing solutions. We propose an interpretable prediction method for university student academic crisis warning, which consists of K-prototype-based student portrait construction and Catboost–SHAP-based academic achievement prediction. The academic crisis warning experiment is carried out on desensitization multi-source student data of a university. The experimental results show that the proposed method has significant advantages over common machine learning algorithms. In terms of achievement prediction, mean square error (MSE) reaches 24.976, mean absolute error (MAE) reaches 3.551, coefficient of determination ($$R^{2}$$ R 2 ) reaches 80.3%. The student portrait and Catboost–SHAP method are used for visual analysis of the academic achievement factors, which provide intuitive decision support and guidance assistance for education administrators.

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A Multimethod Analysis for Average Annual Precipitation Mapping in the Khorasan Razavi Province (Northeastern Iran)

Atmosphere ◽

10.3390/atmos12050592 ◽

2021 ◽

Vol 12 (5) ◽

pp. 592

Author(s):

Mehdi Aalijahan ◽

Azra Khosravichenar

Keyword(s):

Regional Planning ◽

Interpolation Method ◽

Absolute Error ◽

Rain Gauge ◽

Annual Precipitation ◽

Coefficient Of Determination ◽

Distance Weighting ◽

Average Annual Precipitation ◽

Inverse Distance

The spatial distribution of precipitation is one of the most important climatic variables used in geographic and environmental studies. However, when there is a lack of full coverage of meteorological stations, precipitation estimations are necessary to interpolate precipitation for larger areas. The purpose of this research was to find the best interpolation method for precipitation mapping in the partly densely populated Khorasan Razavi province of northeastern Iran. To achieve this, we compared five methods by applying average precipitation data from 97 rain gauge stations in that province for a period of 20 years (1994–2014): Inverse Distance Weighting, Radial Basis Functions (Completely Regularized Spline, Spline with Tension, Multiquadric, Inverse Multiquadric, Thin Plate Spline), Kriging (Simple, Ordinary, Universal), Co-Kriging (Simple, Ordinary, Universal) with an auxiliary elevation parameter, and non-linear Regression. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2) were used to determine the best-performing method of precipitation interpolation. Our study shows that Ordinary Co-Kriging with an auxiliary elevation parameter was the best method for determining the distribution of annual precipitation for this region, showing the highest coefficient of determination of 0.46% between estimated and observed values. Therefore, the application of this method of precipitation mapping would form a mandatory base for regional planning and policy making in the arid to semi-arid Khorasan Razavi province during the future.

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Dynamics of potential precipitation under climate change scenarios at Cameron highlands, Malaysia

SN Applied Sciences ◽

10.1007/s42452-021-04332-x ◽

2021 ◽

Vol 3 (3) ◽

Author(s):

Nuraddeen Mukhtar Nasidi ◽

Aimrun. Wayayok ◽

Ahmad Fikri Abdullah ◽

Muhamad Saufi Mohd Kassim

Keyword(s):

Environmental Sustainability ◽

General Circulation ◽

Greenhouse Gas Emission ◽

Rapid Change ◽

Absolute Error ◽

General Circulation Models ◽

Coefficient Of Determination ◽

Climate Change Scenarios ◽

Precipitation Concentration Index ◽

Cameron Highlands

AbstractPrecipitation is sensitive to increasing greenhouse gas emission which has a significant impact on environmental sustainability. Rapid change of climate variables is often result into large variation in rainfall characteristics which trigger other forms of hazards such as floods, erosion, and landslides. This study employed multi-model ensembled general circulation models (GCMs) approach to project precipitation into 2050s and 2080s periods under four RCPs emission scenarios. Spatial analysis was performed in ArcGIS10.5 environment using Inverse Distance Weighted (IDW) interpolation and Arc-Hydro extension. The model validation indicated by coefficient of determination, Nash–Sutcliffe efficiency, percent bias, root mean square error, standard error, and mean absolute error are 0.73, 0.27, 20.95, 1.25, 0.37 and 0.15, respectively. The results revealed that the Cameron Highlands will experience higher mean daily precipitations between 5.4 mm in 2050s and 9.6 mm in 2080s under RCP8.5 scenario, respectively. Analysis of precipitation concentration index (PCI) revealed that 75% of the watershed has PCI greater than 20 units which indicates substantial variability of the precipitation. Similarly, there is varied spatial distribution patterns of projected precipitation over the study watershed with the largest annual values ranged between 2900 and 3000 mm, covering 71% of the total area in 2080s under RCP8.5 scenario. Owing to this variability in rainfall magnitudes, appropriate measures for environmental protection are essential and to be strategized to address more vulnerable areas.

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Uncertainty Assessment of Urban Hydrological Modelling from a Multiple Objective Perspective

Water ◽

10.3390/w12051393 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1393 ◽

Cited By ~ 2

Author(s):

Bo Pang ◽

Shulan Shi ◽

Gang Zhao ◽

Rong Shi ◽

Dingzhi Peng ◽

...

Keyword(s):

Flood Management ◽

Uncertainty Assessment ◽

Multiple Objective ◽

Hydrological Models ◽

Validation Period ◽

Urban Flood ◽

Calibration Period ◽

Storm Water Management Model ◽

Glue Method ◽

Generalized Likelihood Uncertainty Estimation

The uncertainty assessment of urban hydrological models is important for understanding the reliability of the simulated results. To satisfy the demand for urban flood management, we assessed the uncertainty of urban hydrological models from a multiple-objective perspective. A multiple-criteria decision analysis method, namely, the Generalized Likelihood Uncertainty Estimation-Technique for Order Preference by Similarity to Ideal Solution (GLUE-TOPSIS) was proposed, wherein TOPSIS was adopted to measure the likelihood within the GLUE framework. Four criteria describing different urban stormwater characteristics were combined to test the acceptability of the parameter sets. The TOPSIS was used to calculate the aggregate employed in the calculation of the aggregate likelihood value. The proposed method was implemented in the Storm Water Management Model (SWMM), which was applied to the Dahongmen catchment in Beijing, China. The SWMM model was calibrated and validated based on the three and two flood events respectively downstream of the Dahongmen catchment. The results showed that the GLUE-TOPSIS provided a more precise uncertainty boundary compared with the single-objective GLUE method. The band widths were reduced by 7.30 m3/s in the calibration period, and by 7.56 m3/s in the validation period. The coverages increased by 20.3% in the calibration period, and by 3.2% in the validation period. The median estimates improved, with an increase of the Nash–Sutcliffe efficiency coefficients by 1.6% in the calibration period, and by 10.0% in the validation period. We conclude that the proposed GLUE-TOPSIS is a valid approach to assess the uncertainty of urban hydrological model from a multiple objective perspective, thereby improving the reliability of model results in urban catchment.

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Alometric Model for Estimation of Leaf Area of Garcinia brasiliensis Mart. Through Non-destructive Method

Journal of Agricultural Science ◽

10.5539/jas.v11n10p154 ◽

2019 ◽

Vol 11 (10) ◽

pp. 154

Author(s):

Vinicius de Souza Oliveira ◽

Cássio Francisco Moreira de Carvalho ◽

Juliany Morosini França ◽

Flávia Barreto Pinto ◽

Karina Tiemi Hassuda dos Santos ◽

...

Keyword(s):

Leaf Area ◽

Linear Equations ◽

Absolute Error ◽

Coefficient Of Determination ◽

Maximum Width ◽

Linear Dimensions ◽

Length Width ◽

Independent Variable ◽

The Mean ◽

Non Destructive

The objective of the present study was to test and establish mathematical models to estimate the leaf area of Garcinia brasiliensis Mart. through linear dimensions of the length, width and product of both measurements. In this way, 500 leaves of trees with age between 4 and 6 years were collected from all the cardinal points of the plant in the municipality of São Mateus, North of the State of Espírito Santo, Brazil. The length (L) along the main midrib, the maximum width (W), the product of the length with the width (LW) and the observed leaf area (OLA) were obtained for all leaves. From these measurements were adjusted linear equations of first degree, quadratic and power, in which OLA was used as dependent variable as function of L, W and LW as independent variable. For the validation, the values of L, W and LW of 100 random leaves were substituted in the equations generated in the modeling, thus obtaining the estimated leaf area (ELA). The values of the means of ELA and OLA were tested by Student’s t test 5% of probability. The mean absolute error (MAE), root mean square error (RMSE) and Willmott’s index d for all proposed models were also determined. The choice of the best model was based on the non significant values in the comparison of the means of ELA and OLA, values of MAE and RMSE closer to zero and value of the index d and coefficient of determination (R2) close to unity. The equation that best estimates leaf area of Garcinia brasiliensis Mart. in a way non-destructive is the power model represented by por ELA = 0.7470(LW)0.9842 and R2 = 0.9949.

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Impacts of spatial resolution and representation of flow connectivity on large-scale simulation of floods

10.5194/hess-2016-620 ◽

2017 ◽

Cited By ~ 1

Author(s):

Cherry May R. Mateo ◽

Dai Yamazaki ◽

Hyungjun Kim ◽

Adisorn Champathong ◽

Jai Vaze ◽

...

Keyword(s):

Spatial Resolution ◽

Large Scale ◽

Flood Hazard ◽

Regional Scale ◽

Global Scale ◽

Test Case ◽

Efficiency Coefficient ◽

Computational Power ◽

Extreme Flood ◽

Simulation Results

Abstract. Global-scale River Models (GRMs) are core tools for providing consistent estimates of global flood hazard, especially in data-scarce regions. Due to former limitations in computational power and input datasets, most GRMs have been developed to use simplified representation of flow physics and run at coarse spatial resolutions. With increasing computational power and improved datasets, the application of GRMs to finer resolutions is becoming a reality. To support development in this direction, the suitability of GRMs for application to finer resolutions needs to be assessed. This study investigates the impacts of spatial resolution and flow connectivity representation on the predictive capability of a GRM, CaMa-Flood, in simulating the 2011 extreme flood in Thailand. Analyses show that when single downstream connectivity (SDC) is assumed, simulation results deteriorate with finer spatial resolution; Nash–Sutcliffe Efficiency coefficient decreased by more than 35 % between simulation results at 10 km resolution and 1 km resolution. When multiple downstream connectivity (MDC) is represented, simulation results slightly improve with finer spatial resolution. The SDC simulations result in excessive backflows on very flat floodplains due to the restrictive flow directions in finer resolutions. MDC channels attenuated these effects by maintaining flow connectivity and flow capacity between floodplains in varying spatial resolutions. While a regional-scale flood was chosen as a test case, these findings are universal and can be extended to global-scale simulations. These results demonstrate that a GRM can be used for higher resolution simulations of large-scale floods, provided that MDC in rivers and floodplains is adequately represented in the model structure.

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